A healthcare enterprise is made up of one or more related healthcare facilities and may also be associated with other entities not included in the healthcare enterprise. Healthcare information systems store information related to patients of the healthcare enterprise. The patient information is exchanged over a communications network interconnecting the various facilities in the healthcare enterprise. Some such information is easily handled by computing systems: for example, demographic information such as patient name, age, sex, race, address, etc.; and fiscal data such as coding, billing, collections, appeals, etc. However, the most critical part of the patient record is the medical information: for example, clinical information such as symptoms, signs, side effects, complications, etc.; and outcomes such as performance, effectiveness, efficiency, etc. To be useful, these records need to be searchable on the medical terms in them, either for the particular patient (e.g., to apply best practice rules) or over a population of patients (e.g., to develop best practice rules).
However, one problem is that in medical records different terms may be used by different facilities and/or doctors for the same or similar concepts. For example, the terms: “heart is enlarged”, “enlarged heart”, “heart shows enlargement”, and “cardiac enlargement” have the same meaning, and may be translated to a canonical or regular term: “enlarged heart”. Similarly, the same term may have different meanings to different facilities within the healthcare enterprise. For example, “cystic disease” has one meaning in X-ray diagnosis and a different meaning in mammography.
In order to provide complete and accurate results when searching for records related to a medical concept, all terms which may be used to represent that concept need to be found in the search. To facilitate this function, each healthcare information system within the healthcare enterprise maintains a lexicon or dictionary which contains a repository of medical terms (i.e. words or phrases) which may be used in medical transactions in that facility. Searches may then be performed by looking up the regular term associated with each search term, analyzing the medical records to identify the regular terms associated with the terms used in the medical record, and performing the search using the regular terms, all in a known manner.
US Patent publication 2002/0082868, published Jun. 27, 2002 for Pories et al., relates to a system for creating an electronic medical record. A general illness for a patient is first identified and supplied to the system via an input device. In response, a series of screen images are presented to the doctor displaying a plurality of terms, which had been pre-entered into a lexicon, related to the illness. The doctor selects from among the displayed list of terms to describe the results of his examination. In response to the doctor's selections, other screen images with other lists of terms, related to those previously selected, may be displayed, and the doctor may select from among those displayed, until the required level of detail is reached. All of the selected terms are then processed to automatically generate structured medical information for the medical record. A term may be manually added to the lexicon, possibly after the approval of a medical director or other person of authority. For example, if a desired term is not in the lexicon, a doctor may request that it be added to the lexicon. In addition, medical texts may be scanned and parsed, and terms found in the texts automatically added to the lexicon. Also, third party sources, such as hospital, insurance, and/or federal agency databases, may be scanned and parsed to extract relevant terms and the extracted terms added to the lexicon. Further automatic processes may be performed to delete terms which have fallen into disuse.
U.S. Pat. No. 6,055,494, issued Apr. 25, 2000 to Friedman, discloses a system for parsing natural language medical records. A natural language medical record is parsed and its terms compared to entries in the database to assign a canonical term for any natural language expression corresponding to that canonical term. A database is used to hold information necessary to perform the parse. The regular terms resulting from the parsing of the natural language medical record are then further processed. For example, they may be stored in a database record so that all such medical records may be searched using the regular terms.
U.S. Pat. No. 5,809,476, issued Sep. 15, 1998 to Ryan, discloses a system for generating coded data from natural language medical records. Each term in the medical record, and relationships between terms, are analyzed to generate a compressed symbolic representation of the original information. The system provides for correction and/or supplement of the original information. This coded information may later be interrogated.
By parsing and/or coding medical records to identify regular or canonical terms for the medical terms being recorded, the future searching of such records is facilitated, best practice rules may be defined using these terms, and more meaningful statistical analyses of the medical records may be performed.
In Pories et al. only those terms in the lexicon are displayed and made available for a doctor to include in his medical record. In the other systems, only the terms in the lexicon are recognized and coded. Thus, all of the above mentioned systems require a populated database or lexicon to operate. However, the population of such a lexicon can be a time consuming and lengthy process, which may unduly restrict doctors when delivering health care and creating their own medical records. Also, even if automatically created from prior lexicons or from literature, there is no is no assurance that terms collected are relevant to the facility or doctor.
In Pories et al., a doctor may request a term be added to the lexicon. But the update is made manually, possibly only upon approval of a medical director. This system may also automatically delete terms which have not been used for some predetermined time. Neither Friedman nor Ryan disclose dynamically updating the lexicon in response to use of the system.
The requirement for a full lexicon means that such systems require an installation of such a lexicon before they may be used. Installation of a full lexicon may be accomplished in two ways: by moving a lexicon from one or more remote locations to the current health information system; and/or by manually entering the desired terms into the lexicon. The former requires that programs be written which can read the pre-existing lexicon in its format, and write the new lexicon in its, possibly different, format. The development of this program, its testing and execution require a substantial amount of time, and risks carrying over old, unused terms from the old lexicon into the new one. The latter takes even more time, and has a very high probability of the new lexicon having errors, omissions and inaccuracies.
A system which does not require a full lexicon to begin operation, provides a means to rapidly and accurately build a significant portion thereof, and which provides for full and accurate searching of medical records is desirable.